Methods, systems, apparatuses, and devices for facilitating managing incidents occurring in areas monitored by low data-rate monitoring devices using the low data-rate monitoring devices

ABSTRACT

Disclosed herein is a system for facilitating managing incidents occurring in areas monitored by low data-rate monitoring devices using the low data-rate monitoring devices, in accordance with some embodiments. Accordingly, the system comprises a processor, a device server, and a data visualization device. Further, a camera of a low data-rate monitoring device is configured for capturing video of an area. Further, the processor comprises a machine learning (ML) hardware accelerator configured for performing machine learning processing of the video. Further, the processor is configured for generating processed data. Further, the device server is configured for receiving the processed data based on the transmitting of the processed data from a low data-rate transceiver of the low data-rate monitoring device and transmitting a notification to a device. Further, the data visualization device is configured for visualizing the processed data, identifying an incident in the area, and generating the notification for the incident.

The current application claims a priority to the U.S. Provisional Patentapplication Ser. No. 63/026,678 filed on May 14, 2020.

FIELD OF THE INVENTION

Generally, the present disclosure relates to the field of dataprocessing. More specifically, the present disclosure relates tomethods, systems, apparatuses, and devices for facilitating managingincidents occurring in areas monitored by low data-rate monitoringdevices using the low data-rate monitoring devices.

BACKGROUND OF THE INVENTION

AI may bring out possibilities to enable usage scenarios like logistics,industrial 4.0, fire detection, parking management with low data-ratedevices (ex: transmission range 20 km and battery life of 3 years), andthese devices are usually installed and operated in spaces like rooftop,basements or streets. These devices carried with ML can be referred toas AI devices which may represent a significant growth opportunity forlow data-rate devices, including security cameras, parking cameras, firedetection cameras that may span with computing video and

Therefore, there is a need for improved methods, systems, apparatuses,and devices for facilitating managing incidents occurring in areasmonitored by low data-rate monitoring devices using the low data-ratemonitoring devices that may overcome one or more of the above-mentionedproblems and/or limitations.

SUMMARY OF THE INVENTION

This summary is provided to introduce a selection of concepts in asimplified form, that are further described below in the DetailedDescription. This summary is not intended to identify key features oressential features of the claimed subject matter. Nor is this summaryintended to be used to limit the claimed subject matter's scope.

Disclosed herein is a system for facilitating managing incidentsoccurring in areas monitored by low data-rate monitoring devices usingthe low data-rate monitoring devices, in accordance with someembodiments. Accordingly, the system may include a processor, a deviceserver, and a data visualization device. Further, the processor may beconfigured to be communicatively coupled with at least one low data-ratemonitoring device. Further, the least one low data-rate monitoringdevice may include at least one camera and a low data-rate transceiver.Further, the at least one camera and the low data-rate transceiver maybe communicatively coupled. Further, the at least one camera may beconfigured for capturing at least one video of at least one area.Further, the processor may include a machine learning (ML) hardwareaccelerator. Further, the ML hardware accelerator may be configured forperforming machine learning processing of the at least one video forperforming video recognition based on the capturing. Further, theprocessor may be configured for generating processed data based on theperforming of the machine learning processing. Further, the deviceserver may be communicatively coupled with the least one low data-ratemonitoring device. Further, the low data-rate transceiver may beconfigured for transmitting the processed data to the device server.Further, the device server may be configured for receiving the processeddata based on the transmitting of the processed data. Further, thedevice server may be configured for transmitting a notification to atleast one device. Further, the data visualization device may becommunicatively coupled with the device server. Further, the datavisualization device may be configured for visualizing the processeddata. Further, the data visualization device may be configured foridentifying an incident in the at least one area based on thevisualizing. Further, the data visualization device may be configuredfor generating the notification for the incident based on theidentifying.

Further disclosed herein is a system for facilitating managing incidentsoccurring in areas monitored by low data-rate monitoring devices usingthe low data-rate monitoring devices, in accordance with someembodiments. Accordingly, the system may include a processor, a deviceserver, a data visualization device, a controller, and a memory device.Further, the processor may be configured to be communicatively coupledwith at least one low data-rate monitoring device. Further, the leastone low data-rate monitoring device may include at least one camera anda low data-rate transceiver. Further, the at least one camera and thelow data-rate transceiver may be communicatively coupled. Further, theat least one camera may be configured for capturing at least one videoof at least one area. Further, the processor may include a machinelearning (ML) hardware accelerator. Further, the ML hardware acceleratormay be configured for performing machine learning processing of the atleast one video for performing video recognition based on the capturing.Further, the processor may be configured for generating processed databased on the performing of the machine learning processing. Further, thedevice server may be communicatively coupled with the least one lowdata-rate monitoring device. Further, the low data-rate transceiver maybe configured for transmitting the processed data to the device server.Further, the device server may be configured for receiving the processeddata based on the transmitting of the processed data. Further, thedevice server may be configured for transmitting a notification to atleast one device. Further, the data visualization device may becommunicatively coupled with the device server. Further, the datavisualization device may be configured for visualizing the processeddata. Further, the data visualization device may be configured foridentifying an incident in the at least one area based on thevisualizing. Further, the data visualization device may be configuredfor generating the notification for the incident based on theidentifying. Further, the controller may be communicatively coupled withthe processor. Further, the controller may be configured for activatingthe ML hardware accelerator based on a ML accelerator schedule. Further,the performing of the machine learning processing of the at least onevideo may be based on the activating of the ML hardware accelerator.Further, the memory device may be communicatively coupled with thecontroller. Further, the memory device may be configured for storing theML accelerator schedule.

Both the foregoing summary and the following detailed descriptionprovide examples and are explanatory only. Accordingly, the foregoingsummary and the following detailed description should not be consideredto be restrictive. Further, features or variations may be provided inaddition to those set forth herein. For example, embodiments may bedirected to various feature combinations and sub-combinations describedin the detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate various embodiments of the presentdisclosure. The drawings contain representations of various trademarksand copyrights owned by the Applicants. In addition, the drawings maycontain other marks owned by third parties and are being used forillustrative purposes only. All rights to various trademarks andcopyrights represented herein, except those belonging to theirrespective owners, are vested in and the property of the applicants. Theapplicants retain and reserve all rights in their trademarks andcopyrights included herein, and grant permission to reproduce thematerial only in connection with reproduction of the granted patent andfor no other purpose.

Furthermore, the drawings may contain text or captions that may explaincertain embodiments of the present disclosure. This text is included forillustrative, non-limiting, explanatory purposes of certain embodimentsdetailed in the present disclosure.

FIG. 1 is a block diagram of a system for facilitating video recognitionusing AI devices, in accordance with some embodiments.

FIG. 2 is a block diagram of a system for facilitating video recognitionusing the AI devices, in accordance with some embodiments.

FIG. 3 illustrates a timing diagram associated with the system forfacilitating the video recognition, in accordance with some embodiments.

FIG. 4 illustrates security protocols employed in the system forfacilitating the video recognition, in accordance with some embodiments.

FIG. 5 illustrates a timing diagram for establishing a session betweendevices of the system, in accordance with some embodiments.

FIG. 6 is a block diagram of an AI training platform with the AI deviceand video/image dataset for facilitating the video recognition, inaccordance with some embodiments.

FIG. 7 is a block diagram of the AI device of the system, in accordancewith some embodiments.

FIG. 8 is a flowchart of a method for facilitating the video recognitionusing the system, in accordance with some embodiments.

FIG. 9 is a flowchart of a method for facilitating the video recognitionusing the system, in accordance with some embodiments.

FIG. 10 is a block diagram of the AI device of the system, in accordancewith some embodiments.

FIG. 11 is an illustration of an online platform consistent with variousembodiments of the present disclosure.

FIG. 12 is a block diagram of a system for facilitating managingincidents occurring in areas monitored by low data-rate monitoringdevices using the low data-rate monitoring devices, in accordance withsome embodiments.

FIG. 13 is a block diagram of the system with the at least one lowdata-rate monitoring device, in accordance with some embodiments.

FIG. 14 is a block diagram of the system, in accordance with someembodiments.

FIG. 15 is a block diagram of the system, in accordance with someembodiments.

FIG. 16 is a block diagram of the system with the at least one lowdata-rate monitoring device, in accordance with some embodiments.

FIG. 17 is a block diagram of a system for facilitating managingincidents occurring in areas monitored by low data-rate monitoringdevices using the low data-rate monitoring devices, in accordance withsome embodiments.

FIG. 18 is a block diagram of a computing device for implementing themethods disclosed herein, in accordance with some embodiments.

DETAIL DESCRIPTIONS OF THE INVENTION

As a preliminary matter, it will readily be understood by one havingordinary skill in the relevant art that the present disclosure has broadutility and application. As should be understood, any embodiment mayincorporate only one or a plurality of the above-disclosed aspects ofthe disclosure and may further incorporate only one or a plurality ofthe above-disclosed features. Furthermore, any embodiment discussed andidentified as being “preferred” is considered to be part of a best modecontemplated for carrying out the embodiments of the present disclosure.Other embodiments also may be discussed for additional illustrativepurposes in providing a full and enabling disclosure. Moreover, manyembodiments, such as adaptations, variations, modifications, andequivalent arrangements, will be implicitly disclosed by the embodimentsdescribed herein and fall within the scope of the present disclosure.

Accordingly, while embodiments are described herein in detail inrelation to one or more embodiments, it is to be understood that thisdisclosure is illustrative and exemplary of the present disclosure, andare made merely for the purposes of providing a full and enablingdisclosure. The detailed disclosure herein of one or more embodiments isnot intended, nor is to be construed, to limit the scope of patentprotection afforded in any claim of a patent issuing here from, whichscope is to be defined by the claims and the equivalents thereof. It isnot intended that the scope of patent protection be defined by readinginto any claim limitation found herein and/or issuing here from thatdoes not explicitly appear in the claim itself.

Thus, for example, any sequence(s) and/or temporal order of steps ofvarious processes or methods that are described herein are illustrativeand not restrictive. Accordingly, it should be understood that, althoughsteps of various processes or methods may be shown and described asbeing in a sequence or temporal order, the steps of any such processesor methods are not limited to being carried out in any particularsequence or order, absent an indication otherwise. Indeed, the steps insuch processes or methods generally may be carried out in variousdifferent sequences and orders while still falling within the scope ofthe present disclosure. Accordingly, it is intended that the scope ofpatent protection is to be defined by the issued claim(s) rather thanthe description set forth herein.

Additionally, it is important to note that each term used herein refersto that which an ordinary artisan would understand such term to meanbased on the contextual use of such term herein. To the extent that themeaning of a term used herein—as understood by the ordinary artisanbased on the contextual use of such term—differs in any way from anyparticular dictionary definition of such term, it is intended that themeaning of the term as understood by the ordinary artisan shouldprevail.

Furthermore, it is important to note that, as used herein, “a” and “an”each generally denotes “at least one,” but does not exclude a pluralityunless the contextual use dictates otherwise. When used herein to join alist of items, “or” denotes “at least one of the items,” but does notexclude a plurality of items of the list. Finally, when used herein tojoin a list of items, “and” denotes “all of the items of the list.”

The following detailed description refers to the accompanying drawings.Wherever possible, the same reference numbers are used in the drawingsand the following description to refer to the same or similar elements.While many embodiments of the disclosure may be described,modifications, adaptations, and other implementations are possible. Forexample, substitutions, additions, or modifications may be made to theelements illustrated in the drawings, and the methods described hereinmay be modified by substituting, reordering, or adding stages to thedisclosed methods. Accordingly, the following detailed description doesnot limit the disclosure. Instead, the proper scope of the disclosure isdefined by the claims found herein and/or issuing here from. The presentdisclosure contains headers. It should be understood that these headersare used as references and are not to be construed as limiting upon thesubjected matter disclosed under the header.

The present disclosure includes many aspects and features. Moreover,while many aspects and features relate to, and are described in thecontext of methods, systems, apparatuses, and devices for facilitatingmanaging incidents occurring in areas monitored by low data-ratemonitoring devices using the low data-rate monitoring devices,embodiments of the present disclosure are not limited to use only inthis context.

In general, the method disclosed herein may be performed by one or morecomputing devices. For example, in some embodiments, the method may beperformed by a server computer in communication with one or more clientdevices over a communication network such as, for example, the Internet.In some other embodiments, the method may be performed by one or more ofat least one server computer, at least one client device, at least onenetwork device, at least one sensor, and at least one actuator. Examplesof the one or more client devices and/or the server computer mayinclude, a desktop computer, a laptop computer, a tablet computer, apersonal digital assistant, a portable electronic device, a wearablecomputer, a smartphone, an Internet of Things (IoT) device, a smartelectrical appliance, a video game console, a rack server, asuper-computer, a mainframe computer, mini-computer, micro-computer, astorage server, an application server (e.g. a mail server, a web server,a real-time communication server, an FTP server, a virtual server, aproxy server, a DNS server, etc.), a quantum computer, and so on.Further, one or more client devices and/or the server computer may beconfigured for executing a software application such as, for example,but not limited to, an operating system (e.g. Windows, Mac OS, Unix,Linux, Android, etc.) in order to provide a user interface (e.g. GUI,touch-screen based interface, voice based interface, gesture basedinterface, etc.) for use by the one or more users and/or a networkinterface for communicating with other devices over a communicationnetwork. Accordingly, the server computer may include a processingdevice configured for performing data processing tasks such as, forexample, but not limited to, analyzing, identifying, determining,generating, transforming, calculating, computing, compressing,decompressing, encrypting, decrypting, scrambling, splitting, merging,interpolating, extrapolating, redacting, anonymizing, encoding anddecoding. Further, the server computer may include a communicationdevice configured for communicating with one or more external devices.The one or more external devices may include, for example, but are notlimited to, a client device, a third party database, a public database,a private database, and so on. Further, the communication device may beconfigured for communicating with the one or more external devices overone or more communication channels. Further, the one or morecommunication channels may include a wireless communication channeland/or a wired communication channel. Accordingly, the communicationdevice may be configured for performing one or more of transmitting andreceiving of information in electronic form. Further, the servercomputer may include a storage device configured for performing datastorage and/or data retrieval operations. In general, the storage devicemay be configured for providing reliable storage of digital information.Accordingly, in some embodiments, the storage device may be based ontechnologies such as, but not limited to, data compression, data backup,data redundancy, deduplication, error correction, data finger-printing,role based access control, and so on.

Further, one or more steps of the method disclosed herein may beinitiated, maintained, controlled, and/or terminated based on a controlinput received from one or more devices operated by one or more userssuch as, for example, but not limited to, an end user, an admin, aservice provider, a service consumer, an agent, a broker and arepresentative thereof. Further, the user as defined herein may refer toa human, an animal, or an artificially intelligent being in any state ofexistence, unless stated otherwise, elsewhere in the present disclosure.Further, in some embodiments, the one or more users may be required tosuccessfully perform authentication in order for the control input to beeffective. In general, a user of the one or more users may performauthentication based on the possession of a secret human readable secretdata (e.g. username, password, passphrase, PIN, secret question, secretanswer, etc.) and/or possession of a machine readable secret data (e.g.encryption key, decryption key, bar codes, etc.) and/or or possession ofone or more embodied characteristics unique to the user (e.g. biometricvariables such as, but not limited to, fingerprint, palm-print, voicecharacteristics, behavioral characteristics, facial features, irispattern, heart rate variability, evoked potentials, brain waves, and soon) and/or possession of a unique device (e.g. a device with a uniquephysical and/or chemical and/or biological characteristic, a hardwaredevice with a unique serial number, a network device with a uniqueIP/MAC address, a telephone with a unique phone number, a smartcard withan authentication token stored thereupon, etc.). Accordingly, the one ormore steps of the method may include communicating (e.g. transmittingand/or receiving) with one or more sensor devices and/or one or moreactuators in order to perform authentication. For example, the one ormore steps may include receiving, using the communication device, thesecret human readable data from an input device such as, for example, akeyboard, a keypad, a touch-screen, a microphone, a camera, and so on.Likewise, the one or more steps may include receiving, using thecommunication device, the one or more embodied characteristics from oneor more biometric sensors.

Further, one or more steps of the method may be automatically initiated,maintained, and/or terminated based on one or more predefinedconditions. In an instance, the one or more predefined conditions may bebased on one or more contextual variables. In general, the one or morecontextual variables may represent a condition relevant to theperformance of the one or more steps of the method. The one or morecontextual variables may include, for example, but are not limited to,location, time, identity of a user associated with a device (e.g. theserver computer, a client device, etc.) corresponding to the performanceof the one or more steps, environmental variables (e.g. temperature,humidity, pressure, wind speed, lighting, sound, etc.) associated with adevice corresponding to the performance of the one or more steps,physical state and/or physiological state and/or psychological state ofthe user, physical state (e.g. motion, direction of motion, orientation,speed, velocity, acceleration, trajectory, etc.) of the devicecorresponding to the performance of the one or more steps and/orsemantic content of data associated with the one or more users.Accordingly, the one or more steps may include communicating with one ormore sensors and/or one or more actuators associated with the one ormore contextual variables. For example, the one or more sensors mayinclude, but are not limited to, a timing device (e.g. a real-timeclock), a location sensor (e.g. a GPS receiver, a GLONASS receiver, anindoor location sensor, etc.), a biometric sensor (e.g. a fingerprintsensor), an environmental variable sensor (e.g. temperature sensor,humidity sensor, pressure sensor, etc.) and a device state sensor (e.g.a power sensor, a voltage/current sensor, a switch-state sensor, a usagesensor, etc. associated with the device corresponding to performance ofthe or more steps).

Further, the one or more steps of the method may be performed one ormore number of times. Additionally, the one or more steps may beperformed in any order other than as exemplarily disclosed herein,unless explicitly stated otherwise, elsewhere in the present disclosure.Further, two or more steps of the one or more steps may, in someembodiments, be simultaneously performed, at least in part. Further, insome embodiments, there may be one or more time gaps between performanceof any two steps of the one or more steps.

Further, in some embodiments, the one or more predefined conditions maybe specified by the one or more users. Accordingly, the one or moresteps may include receiving, using the communication device, the one ormore predefined conditions from one or more, and devices operated by theone or more users. Further, the one or more predefined conditions may bestored in the storage device. Alternatively, and/or additionally, insome embodiments, the one or more predefined conditions may beautomatically determined, using the processing device, based onhistorical data corresponding to performance of the one or more steps.For example, the historical data may be collected, using the storagedevice, from a plurality of instances of performance of the method. Suchhistorical data may include performance actions (e.g. initiating,maintaining, interrupting, terminating, etc.) of the one or more stepsand/or the one or more contextual variables associated therewith.Further, machine learning may be performed on the historical data inorder to determine the one or more predefined conditions. For instance,machine learning on the historical data may determine a correlationbetween one or more contextual variables and performance of the one ormore steps of the method. Accordingly, the one or more predefinedconditions may be generated, using the processing device, based on thecorrelation.

Further, one or more steps of the method may be performed at one or morespatial locations. For instance, the method may be performed by aplurality of devices interconnected through a communication network.Accordingly, in an example, one or more steps of the method may beperformed by a server computer. Similarly, one or more steps of themethod may be performed by a client computer. Likewise, one or moresteps of the method may be performed by an intermediate entity such as,for example, a proxy server. For instance, one or more steps of themethod may be performed in a distributed fashion across the plurality ofdevices in order to meet one or more objectives. For example, oneobjective may be to provide load balancing between two or more devices.Another objective may be to restrict a location of one or more of aninput data, an output data and any intermediate data therebetweencorresponding to one or more steps of the method. For example, in aclient-server environment, sensitive data corresponding to a user maynot be allowed to be transmitted to the server computer. Accordingly,one or more steps of the method operating on the sensitive data and/or aderivative thereof may be performed at the client device.

Overview:

The present disclosure describes methods, systems, apparatuses, anddevices for facilitating managing incidents occurring in areas monitoredby low data-rate monitoring devices using the low data-rate monitoringdevices.

Further, the present disclosure relates to the methods of videorecognition by transmitting a low data-rate radio frequency (RF) signalwith Machine Learning (ML) hardware acceleration. In particular, thepresent disclosure relates to a method about Artificial Intelligence(AI) recognition of video through ML and sending the data by lowdata-rate RF like Lora, NB-IoT, or Sigfox.

In one embodiment, ML is used for video recognition for low data-ratedevices. The video recognition can be provided by ML hardwareaccelerators in AI devices. The AI device captures the video stream andprocesses it into small pieces of data results. The AI device can send asmall amount of processed data to the cloud for data visualizationthrough a low data-rate transceiver.

The AI device is configured to capture the video stream via an MLhardware accelerator that is configured to provide video recognition.For example, if there is a fire outbreak, the ML hardware acceleratorwill provide video recognition that is processed and sent by an AIdevice via the low data-rate transceiver to the cloud for datavisualization. Thus, notifying the system that there is a fire outbreak.

The AI device capturing the video stream can be individually acting orin a group as a cluster of AI devices. The scope of the invention coversthe use of ML for video recognition and the use of AI devices forprocessing the data into segments that can be stored in the database.The data recognition system is used for data visualization andcalculation of local image properties along with AI accelerators. Bigpossibilities can come from analyzing this data.

Further, the present disclosure provides systems for video recognitionusing an AI device. The AI device captures video streams, processes datasignals with Machine Learning (ML) hardware acceleration, and sends thedata using low data-rate radio frequency (RF). In particular, thepresent disclosure relates to a method about Artificial Intelligence(AI) recognition of video through ML and transmission of data by lowdata-rate radiofrequency.

FIG. 1 is a block diagram of a system 100 for facilitating videorecognition using AI devices 101, in accordance with some embodiments.FIG. 1 is a system diagram for video recognition by transmitting a lowdata-rate RF signal with ML hardware acceleration according to someembodiments.

FIG. 1 includes a plurality of AI devices 101, Low data-rategateway/base stations 102, cluster of AI devices 103, data visualization104, database 105, an Internet (e.g., world wide web) 106, device server107, and fetching from database mechanism 121. The low-data rategateway/base stations carry the data/information to the database.

The AI device 101 is a user equipment (UE) configured to communicate viathe low data-rate transceiver. The UE may include any type of computingdevice. Example UEs include security cameras, parking cameras, firedetection cameras, and the like. The UEs may include a plurality ofapplications installed and running on the UEs which may periodicallycommunicate data over the low data-rate devices using lowerradiofrequency.

As used herein, the AI devices 101 send the processed data using alow-data transceiver with transmission as low as 250 KB. For example,the AI devices are equipped with a low data rate radio frequency (RF). Alow data-rate RF like Lora, NB-IoT, or Sigfox can be used. However,Lora, NB-IoT, or Sigfox are used as an example and are not intended tobe limiting. Other types of low-data rate RF can be used instead.

The AI devices 101 can automatically activate their transmission whenthe video is captured. In some embodiments, manual activation can beused. For example, if the AI device is in sleeping mode, an internalconfiguration can be changed automatically or manually.

FIG. 1 illustrates a portion of the AI devices 103 in a cluster andother AI devices not in a cluster 101. At least one of the AI devices incluster 103 may be a master AI device that can distribute the data toother AI devices 103 in the cluster.

The low-data rate transceiver 205 can include hardware and softwareconfigured to dedicatedly receive and send processed data through thelow-data rate gateway/base station 102. Such a low data-rate transceiver205 may be unable to perform other functions other than transmittingdata. The low data-rate transceiver 205 can also be configured withother devices to make it a multipurpose system.

In some embodiments, the AI devices 101 send data through low data-ratetransceiver 205 using low data-rate RF. As used herein, a low data-ratetransceiver 205 is configured to send and receive data. This data isfurther transmitted to the cloud for data storage and visualization.

In some embodiments, the AI devices 101 communicate with the internetthrough low data-rate transceiver 205 via low data-rate gateway/basestations 102.

The AI devices 101 receives commands through the low data-ratetransceiver 205 from the device server 107 for an internal configurationchange. FIG. 3 further describes this mechanism of information transferto the AI devices.

The device server 107 provides commands to the AI devices 101 bycommunicating via internet 106. The commands can travel from the deviceserver 107 via internet 106 and through the low data-rate gateway/basestations 102 to the AI devices.

In some embodiments, the data visualization device can employ differentprotocols to communicate with the AI devices 101 using device server107. The data visualization device can also communicate with the AIdevices 101 to establish an authenticated session and/or to establish asecure session. An authenticated session and the secure session arefurther described in FIG. 5

FIG. 2 is a block diagram of a system 200 for facilitating videorecognition using the AI devices 101, in accordance with someembodiments. FIG. 2 is a system diagram for video recognition bytransmitting a low data-rate RF signal with ML hardware accelerationaccording to one embodiment. FIG. 2. includes people (e.g., for peoplecounting application) 203, camera equipment 204, microcontroller (Nonumber given to the microcontroller—212), Low Data-rate transceiver 205,Antenna 206, Machine Learning (ML) Accelerator 207, Low Data-rategateway/base 208 which is analogous to Low Data-rate gateway/base 102,Internet 209 which is analogous to internet 106, data visualization 211,database 210 which is analogous to database 105, a device server 241which is analogous to the device server 107.

The AI device can include camera 204, microcontroller 212, low data-ratetransceiver 205, antenna 206, Machine learning (ML) accelerator 207.

A low data-rate transceiver 205 is with an antenna 206 totransmit/receive data to/from low data-rate gateway/base stations. A lowdata-rate transceiver can be configured in a way to communicate with themicrocontroller and database system 210 via internet 209.

The AI device is equipped with a low data-rate RF including a lowdata-rate transceiver 205 to send and receive data. This data istransmitted through low data-rate gateway/base stations to the deviceserver 241 via the Internet. The data is visualized in the datavisualization system 211 and all the data is stored in database 210.

In the example provided in FIG. 2, the camera 204 in the AI devicecaptures the video streaming, the microcontroller 212 and ML acceleratorconnected to the camera processes the video stream and sends the data tothe data visualization system through low data-rate transceiver 205. Thelow data-rate transceiver 205 can communicate with the device server 241through low data-rate gateway/base stations 208 and internet 209.

The RF used for communication can be any low data-rate RF like Lora,NB-IoT, or Sigfox.

The low data-rate transceiver 205 can communicate with the low data-rategateway/base stations 208. The low data-rate gateway/base stations 208can communicate with the device server 241 via internet 209. The dataserver 241 can communicate with the database 210 and data visualizationsystem 211.

By the way of not limiting example, people 203 can be used as a sourceof data for the AI device camera 204. For example, in the peoplecounting model, the source is people 203. The data is captured by thecamera 204 and the data is further sent to the ML accelerator 207 forprocessing.

The ML accelerator 207 in the AI device can authenticate itself toregister on the device network of the device server 241. The database210 can store information from people/surrounding 203 captured in theform of a video stream and later processed as data received by the AIdevice.

The database 210 is linked with data visualization 211 and the deviceserver 241. The information from the AI device can be transmitted to thedata visualization system 211 by database 210 and commands can be sentfrom data visualization system 211 to the device via the device server241. FIG. 3 further describes this process.

FIG. 3 illustrates a timing diagram associated with the system 100 forfacilitating the video recognition, in accordance with some embodiments.FIG. 3 is a timing diagram for recognizing a video content with lowdata-rate RF and ML hardware acceleration according to one embodiment.FIG. 3 includes ML Accelerator 304, Low data-rate transceiver 305 whichis analogous to Low data-rate transceiver 205, a device server 306 whichis analogous to the device server 107, data visualization 307 which isanalogous to data visualization 105.

The ML accelerator 304 can activate 331 programmed software withhardware registered on the device network. In some embodiment, the MLaccelerator 304 can communicate with the device server 306 via lowdata-rate transceiver 205 to inform the device server 306 that theprogrammed software with hardware is registered on the device network.

The data visualization 307 can send 310 identified types of the videodata based on the AI (e.g. Fire detection). In some embodiments, thedata visualization 307 determines the type of video data that has beencaptured by the AI device 101. The data visualization 307 cancommunicate with the device server 306 and send 310 identifying the typeof video data captured via different communication mediums (e.g.internet).

The ML accelerator 304 pushes 311 found the recognizable pattern throughML computing to the low data-rate transceiver 305. In some embodiment,the ML accelerator 304 can determine the recognizable pattern by MLcomputing. The ML accelerator 304 sends this information to the lowdata-rate transceiver 305. The low data-rate transceiver 305communicates with the device server 306 by pushing 312 and sends a lowdata-rate packet through gateway/base stations 208 to the server.

In some embodiment, the low data-rate transceiver 305 sends lowdata-rate packets to the device server 306 via gateway/base stationsusing low radio frequency (RF). As used herein, this low RF can be Lora,NB-IoT, Sigfox, or any other low RF with a low data rate (e.g., 250 KB).

The device server 306 pushes data to the service 313. In someembodiment, the device service 306 can forward data received from MLaccelerator 304 via low data-rate transceiver 305 to data visualization307. The low data-rate transceiver 305 communicates with the deviceserver 306 through low data-rate gateway/base stations by sending lowdata-rate packets via low RF.

In some embodiment, the data visualization 307 can request deviceconfiguration 314. The data visualization system can send a request tothe device server 306 for configuring the device (e.g., changing thesleeping time of the AI device).

The device server 306 receives the request 314 and forwards 315 therequest to low data-rate transceiver 305. The low data-rate transceiver305 forwards 316 the request to the ML accelerator 304. That is, thedevice server 306 relays commands to the ML accelerator 304 via thelow-data rate transceiver 305 and low-data rate gateway/base stations208. The ML accelerator 304 receives the request for change in internalconfiguration (e.g., change in sleeping time) via low data-ratetransceiver 305.

In some embodiment, the device server receives 314 the request fordevice configuration. The device server 314 notifies the ML accelerator304 through low data-rate transceiver 305. The device server 306communicates with the low data-rate transceiver by 316 sending downlinkdata through a low data-rate packet. The ML accelerator 304 receives therequest and enables change in internal configuration.

In some embodiments, the AI device 101 can initiate an authenticationsession and/or a secured session using a shared key provided by the datavisualization system 307. FIG. 5 further describes an authenticatedsession and/or a secure session.

The internal configuration of the AI device 107 is changed in responseto the request from data visualization 307. This can ensure automaticchanges in the configuration of the AI device 101 without humanintervention.

FIG. 4 illustrates security protocols employed in the system 100 forfacilitating the video recognition, in accordance with some embodiments.FIG. 4 is a diagram illustrating the security protocols employed inrecognizing a video content with low data-rate RF and ML hardwareacceleration according to one embodiment. FIG. 4 includes datavisualization 404 analogous to data visualization 104, MQTT encrypted405, a device server 406 analogous to the device server 107, lowdata-rate protocol encrypted packets 407, Low data-rate transceiver 408analogous to 205, On-board communication 409 mechanism, ML accelerator421 which is analogous to 207 and secured Video recognition 410.

FIG. 4 also shows security protocols employed to secure communicationsbetween the device server 406 and data visualization 404, device server406 and low data-rate transceiver 408, low data-rate transceiver 408, MLaccelerator 421, and data visualization 404.

For example, communications between data visualization 404 and deviceserver 406 over an internet are secured using MQ Telemetry Transport(MQTT) protocol 405. Communications between device server 406 and lowdata-rate transceiver 408 are secured using low data-rate protocolencrypted packets 407. The AI device includes low data-rate transceiver408 and ML accelerator 421. Communications between Low data-ratetransceiver 408 and ML accelerator 421 are on-board communications thattake place in the AI device.

The data visualization 404 can identify the type of the video data basedon the AI (e.g. Fire detection) and communicate with the ML accelerator421 as described in FIG. 3. The communication between ML accelerator 421and data visualization 404 is secured video recognition 410.

FIG. 5 illustrates a timing diagram for establishing a session betweendevices of the system 100, in accordance with some embodiments. FIG. 5is a timing diagram for establishing an authenticated session accordingto one embodiment. FIG. 5 includes data visualization 505, AI device506. In some embodiments, an authenticated session or/and secure sessionis established before data can be transferred from the datavisualization 505 and/or received from the AI device 506. A lowdata-rate radiofrequency is used to transfer data from the AI device506.

As used herein, an authenticated session describes a session wherein theAI device 506 confirms that the data visualization 505 is authorized totransfer data to the AI device 506. A secured session describes asession wherein the communication between the AI device 506 and datavisualization 505 is secure from third parties.

The data visualization 505 transmits a key 507 to the AI device 506. Theshared key is stored in the data visualization 505 and the Ai device506. In one embodiment, the key 507 can be a random number and/orcharacter.

Establishing a session includes starting a session using a key betweenthe AI device 506 and data visualization 505. The key 507 is stored atboth the ends of communication between AI device 506 and datavisualization 505. The key is used to subscribe to the data 508 from theAI device 506 by the data visualization 505.

In establishing an authenticated session/and or a secure session, the AIdevice 506 can sign data using the key 509. The data can be received bythe data visualization 505 using the key.

A request to initiate a connection 508 can be sent from datavisualization 505 to the AI device 506. Data visualization 505 cansubscribe to the data coming from AI device 506 using the key 508. TheAI device 506 signs the data to the data visualization 505 using the key508.

In some embodiment, the AI device 506 can publish the data to the datavisualization 505. This data is processed by a video stream from MLaccelerator 304. The data is transmitted through low data-ratetransceiver 305 over low data-rate gateway/base stations 107 and viainternet 106 to data visualization system 505.

Video recognition is established 511 when the AI device 506 publishesthe data 510 to the data visualization 505. The video recognition datafrom the data visualization 505 can be sent back to the AI device 506.

FIG. 6 is a block diagram of an AI training platform 635 with the AIdevice 606 and video/image dataset 631 for facilitating the videorecognition, in accordance with some embodiments. FIG. 6 is a blockdiagram showing AI training models according to one embodiment. FIG. 6includes AI device 606, AI training AI 635, Parking model 607, Firedetection model 608, people counting model 609, garbage detection model610, and other models 620, with video/image dataset 631.

In some embodiment, the video stream data received from the AI device606 can be used to train AI models (e.g., parking model, fire detectionmodel). The training can take place on an AI platform 635. The AItraining platform 635 can train various AI models like Parking model607, Fire detection model 608, people counting model 609, garbagedetection model 610, and other models 620.

In some embodiment, the data from the models are stored in theVideo/image dataset 631. This data can be further used to train similarmodels.

In one embodiment, the data from the models and AI device 606 can beutilized by the data visualization 505. This data can be stored in thedatabase 105 for video recognition 511.

FIG. 7 is a block diagram of the AI device 101 of the system 100, inaccordance with some embodiments. FIG. 7 is a block diagram illustratingAI device's circuitry in accordance with various embodiments. FIG. 7includes ROM 707, ML accelerator 708 which is analogous to MLaccelerator 207, antenna 709 which is analogous to antenna 206, lowdata-rate transceiver 710 which is analogous to low data-ratetransceiver 205, camera 711, microcontroller 712 an interface 713, AImodel 714. In some embodiments, the AI device 101 may be a device thatimplements all or part of AI device's functionality as hardware, memory,and/or software. In embodiments, the AI device 101 may also includecamera 711, ROM 707, microcontroller 712, low data-rate transceiver 710,antenna 709, interface 713, ML accelerator 706. In some embodiments, theAI device 101 may be coupled with or include one or more plurality ofantenna elements 709. The AI device 101 and/or components of the AIdevice 101 may be configured to perform operations similar to thosedescribed elsewhere in this disclosure.

In embodiments, where the AI device 101 has a memory 707 (e.g., ROM)that may be coupled to the microcontroller 712 and ML accelerator 708for processing the video stream received from the camera 711. Inembodiments, where the microcontroller is connected to the interface713, the interface can be used for hardware driver circuits and/orexternal connections. As used herein, the AI model 714 is linked to theAI device 101 through the interface 713. In embodiments, where the lowdata-rate transceiver 710 is connected to the microcontroller 712 andantenna 709 for transmission/and or receiving data. The low data-ratetransceiver 710 may be configured to perform operations similar to thosedescribed elsewhere in this disclosure.

FIG. 8 is a flowchart of a method 800 for facilitating the videorecognition using the system 100, in accordance with some embodiments.FIG. 8 is a block diagram illustrating the method 800 for videorecognition by transmitting a low data-rate RF signal with ML hardwareacceleration according to one embodiment. In some embodiments, theelectronic device of FIG. 7 may be configured to perform one or moreprocesses such as processes of FIG. 8. For example, in embodiments wherethe electronic device is, implements, is incorporated into, or isotherwise part of an AI camera, or a portion of an AI camera, theprocess may include receiving 881, by the AI camera, an indication thatthe video is captured from the video interface. The process may furtherinclude connecting 882, the video captured is processed at the Machinelearning accelerator. Results are generated by the ML accelerator. Theprocess may further include connecting 883, results generated are sentthrough low data-rate transceivers. The results produced are to bedisplayed. The process may further include receiving 884, the resultsgenerated by ML accelerator are displayed in the data visualizationplatform. In some examples, the data is stored and used to train AImodels.

FIG. 9 is a flowchart of a method 900 for facilitating the videorecognition using the system 100, in accordance with some embodiments.FIG. 9 is a block diagram illustrating the method 900 for videorecognition by transmitting a low data-rate RF signal with ML hardwareacceleration according to one embodiment. In some embodiments, theelectronic device of FIG. 7 may be configured to perform one or moreprocesses such as processes of FIG. 9. For example, in embodiments wherethe electronic device is, implements, is incorporated into, or isotherwise part of an AI camera, or a portion of an AI camera, theprocess may include receiving 991, by the AI camera, visual content ispre-collected and stored. The process may further include connecting992, wherein the collected visual content is used for training Machinelearning models offline. The process may further include connecting 993,a machine learning image is obtained from the process. This ML image canbe further used for training various AI models. The process may furtherinclude connecting 994, interfaces are used to load the image to the AIdevice. In some examples, the video stream captured by the camera 711and processed by the ML accelerator 708 c in the AI device can be usedto train AI model 714. FIG. 6 describes this process of AI modeltraining.

In some embodiments, the video stream is collected and processed usingthe AI device 101. This processed data is pre-collected visual content991. The data is fed to the offline Machine learning training model 992to train the models. ML image obtained through the process 993 is sentto the AI device through interfaces. Further, the image is loaded to theAI device 994.

FIG. 10 is a block diagram of the AI device 101 of the system 100, inaccordance with some embodiments. FIG. 10 is a block diagramillustrating components of the AI device 101 according to oneembodiment.

In some embodiments, the AI device 101 may include Machine learning (ML)circuitry 1011, application circuitry 1012, Low data-rate circuitry1013, and one or more antennas 1014, coupled together, as shown in FIG.10.

The Machine Learning circuitry 1011 can include hardware accelerators.Hardware accelerators in the Machine Learning circuitry 1011 can be usedto offload computing tasks into specialized hardware components withinthe system.

The Machine Learning circuitry 1011 may include one or more hardwareaccelerators like CPU accelerators. The Machine Learning circuitry 1011may include one or more application processors. By way of non-limitingexample, the Machine Learning circuitry 1011 may include one or moresingle-core or multi-core processors. The Processor(s) may include anycombination of general-purpose processors and dedicated processors(e.g., graphics processors, application processors, etc.). Theprocessor(s) may be operably coupled and/or include memory/storage andmay be configured to execute instructions stored in the memory/storage.

The Machine Learning circuitry 1011 may include hardware acceleratorsthat are reconfigurable devices such as field programming gate arrays(FPGA). The Machine Learning circuitry 1011 may include FPGA forfunctioning of the hardware framework and software interface in the AIdevice 101. The FPGA configuration is generally specified using ahardware description language (HDL). Open computing language (OpenCL)can also be used to function FPGAs.

By way of non-limiting example, the Machine Learning circuitry 1011 mayinclude application specific integrated circuit (ASIC) instead of orwith FPGA for better functioning.

By way of non-limiting example, the ML circuitry 1011 may includegraphics processing units (GPUs) for manipulation of images. GPUs mayalso be used but not limited to the calculation of local imageproperties. GPU accelerated video decoding process can be used toprocess the video stream data collected by the AI device 101.

By way of non-limiting example, the ML circuitry 1011 may includegraphics processing units (GPUs) for manipulation of images. GPUs mayalso be used but not limited to the calculation of local imageproperties. GPU accelerated video decoding process can be used toprocess the video stream data collected by the AI device 101.

By way of non-limiting example, the application circuitry 1012 mayinclude a digital/analog interface. The digital/analog interface mayinclude a mixer and/or a digital signal processor (DSP) and/or anamplifier. The amplifier may be configured to amplify the down-convertedsignals.

In some embodiments, the output signals and the input signals may beanalog signals, although the scope of the embodiments is not limited inthis respect. In some alternative embodiments, the output signals andthe input signals may be digital signals. In such embodiments, theapplication circuitry 1012 may include analog-to-digital converter (ADC)and digital to analog converter (DAC) circuitry, the applicationcircuitry 1012 may include analog/digital interface to communicate withthe low-data rate circuitry 1013.

In some embodiments, the analog/digital interface in the applicationcircuitry 1012 may include one or more digital signal processor(s)(DSP). The DSP(s) may include elements for compression/decompression andecho cancellation and other suitable processing elements.

The application circuitry 1012 may be configured to process datareceived from the low data-rate circuitry 1013. The applicationcircuitry 1012 may also be configured to send data to the low data-ratecircuitry 1013. The application circuitry 1012 may interface with theMachine Learning circuitry 1011 for generating and processing the datareceived from the low data-rate circuitry 1013 using a hardwareaccelerator, and for controlling operations of the Machine learningcircuitry 1011.

The application circuitry 1012 may further include memory/storage. Thememory/storage may include data and/or instructions for operationsperformed by the processors of the application circuitry 1012 storedthereon. In some embodiments, the memory/storage may include anycombination of suitable volatile memory and/or non-volatile memory. Thememory/storage may include any combinations of various levels ofmemory/storage including but not limited to read-only memory (ROM)having embedded software instructions (e.g., firmware), random accessmemory (RAM), cache, buffers, etc. In some embodiments, thememory/storage may be shared among the various processors or dedicatedto particular processors.

Components of the application circuitry 1012 may be suitably combined ina single chip or a single chipset, or disposed on a same circuit boardin some embodiments, some or all of the constituent components of theapplication circuitry 1012 and the ML circuitry 1011 may be implementedtogether, such as, for example, on a system on a chip (SOC).

The low data-rate circuitry 1013 may include one or more modulator(s).The modulator(s) can be used to perform the process of modulation byvarying one or more properties of the periodic waveform (e.g., carriersignal), with the modulating signal that contains information to betransmitted via a low data-rate transceiver. The low data-rate circuitry1013 can also contain a demodulator for the inverse action of modulationor a modulator-demodulator (modem).

In some embodiments, the low data-rate circuitry 1013 may provide forcommunication compatible with low data-rate radio frequencies.Embodiments, in which the low data-rate circuitry 1013 is configured tosupport radio communication.

In some embodiments, low data-rate circuitry 1013 may include an antenna1014 for enabling communication with the wireless networks. The lowdata-rate circuitry 1013 may include a receive signal path which mayinclude circuitry to down-convert the received information from thedevice server over low data-rate gateway/base stations. The lowdata-rate circuitry 1013 may include a transmit signal path which mayinclude circuitry to up-convert data received from the applicationcircuitry 101 and provide output signals to the antenna 1014 fortransmission.

The low data-rate circuitry 1013 may include Radio frontend circuitry.In some mode embodiments, separate radio IC circuitry may be providedfor processing signals, although scope of the embodiments is not limitedin this respect.

In some embodiments, frequency input may be provided by avoltage-controlled oscillator (VCO), although that is not a requirement.The divider control input may be provided by either the low data-ratecircuitry 1013 or the application circuitry 1012 depending on thedesired output.

The low data-rate circuitry 1013 may include circuitry configured tooperate RF signals received from one or more antennas 1014, amplify thereceived signals, provide amplified versions of received signals to theapplication circuitry 1012 for further processing. The low data-ratecircuitry 1013 may include circuitry to transmit signals received fromthe application circuitry 1012 by at least one of the one or moreantennas 1014.

In some embodiments, the low data-rate circuitry 1013 may include a lowdata-rate transceiver with TX/RX switch configured to switch between atransmit mode and a receive mode. The low data-rate circuitry 1013 mayinclude a receive signal path and a transmit signal path. The receivesignal path of the low data-rate circuitry may include a low-noiseamplifier (LNA) to amplify received signals and filters.

The low data-rate circuitry 1013 may include a Radio frontend that mayhandle various radio control functions that enable communication. By wayof non-limiting example, the radio control functions may include signalmodulation/demodulation. Encoding/decoding, radio frequency shifting,other functions, and combinations thereof.

In some embodiments, the low data-rate circuitry 1013 may be programmedto perform Fast-Fourier Transform (FFT), other functions, andcombinations thereof. The low data-rate circuitry 1013 may be configuredto carry out frequency shift keying (FSK) modulation scheme. By way ofnon-limiting example, the low data-rate circuitry 1013 can be configuredto use any kind of modulation scheme (e.g., FSK, BFSK).

In some embodiments, the radio front end circuitry may includeprecoding, constellation, mapping/demapping functions, other functions,and combinations thereof. In some embodiments, encoding/decodingcircuitry may be programmed to perform convolutions, tail-bitingconvolutions, encoder/decoder functions, other functions, andcombinations thereof.

In some embodiments, the AI device may be configured to perform one ormore processes, techniques, and/or methods as described herein, orportions thereof.

According to some aspects, an apparatus for an Artificial Intelligence(AI) device, is disclosed. The apparatus includes a camera to record thevideo stream, a ML accelerator for processing the video stream, and alow data-rate transceiver to receive and send processed data. Theapparatus includes memory. The apparatus also includes a microcontrollerdesigned to access the memory. The microcontroller is designed toactivate the processing of the ML accelerator and low data-ratetransceiver communication.

According to further aspects, the AI device uses ML accelerator toprocess the video stream and sends the data to the database via the lowdata-rate transceiver.

According to further aspects, the low data-rate transceiver implementslow data-rate transmission using low data-rate radiofrequency RF likeLora, NB-IoT, or Sigfox.

According to further aspects, the ML accelerator and microcontroller arefurther designed to process the video stream captured through thecamera.

According to further aspects, the one or more processors are designed tofurther send the processed data using ML accelerator via the lowdata-rate transceiver.

According to further aspects, the one or more processors are designed toreceive the data from the database via the low data-rate transceiver.

According to further aspects, e the AI device is also used for trainingother AI models using an AI training platform.

According to further aspects, the ML accelerator communicates with thedevice server to register on the device network with programmed softwareand hardware.

According to further aspects, the data visualization identifies thevideo data and communicates with the AI device via the device server.

According to further aspects, the ML accelerator finds the recognizablepattern through ML computing. According to further aspects, the videorecognition information from the database is communicated to the AIdevice via low data-rate gateway/base stations by the low data-ratetransceiver.

According to further aspects, the low data-rate transceiver sends a lowdata-rate packet through gateway/base stations to the device server.

According to further aspects, the internal configuration of the AIdevice is changed as per instructions by the device server.

According to some aspects, the apparatus is for a device, is disclosed.Further, the apparatus includes an AI device with a low data-ratetransceiver to receive and send data. The apparatus includes memory,data visualization. The apparatus includes one or more processorsdesigned to access the memory, the low data-rate transceiver. Themicrocontroller is designed to receive downlink data through a lowdata-rate network.

According to further aspects, the communication between the AI deviceand Data visualization is a secured session.

According to further aspects, the authentication is established using akey between the AI device and data visualization. According to furtheraspects, the key can be a random number and/or combination of charactersand numbers.

According to further aspects, the data visualization subscribes to thedata sent by the AI device. The AI device signs the data using the key.

According to further aspects, after signing data is published by the AIdevice towards the data visualization by the AI device.

According to further aspects, the video recognition is established onboth the ends of the communication between the AI device and datavisualization.

According to further aspects, the data is MQTT encrypted when sentand/or received from the device server to and/or received from the datavisualization.

According to further aspects, the communication between the deviceserver and low data-rate transceiver takes place by sending and/orreceiving low data-rate protocol encrypted packets.

According to further aspects, the on-board communication between MLaccelerator and the low data-rate transceiver is on-board.

According to further aspects, the video stream recognition informationis generated by the data visualization.

According to some aspects, a method is disclosed. The method includesvideo recognition by transmitting a low data-rate radio frequency (RF)signal with Machine Learning (ML) hardware acceleration. In particular,this method is about Artificial Intelligence (AI) recognition of videothrough ML and transmission of the data by low data-rate RF like Lora,NB-IoT, or Sigfox.

According to further aspects, the AI device uses ML accelerator toprocess the video stream and sends the data to the database via a lowdata-rate transceiver.

According to further aspects, the low data-rate transceiver implementslow data-rate transmission using low data-rate radiofrequency RF likeLora, NB-IoT, or Sigfox.

According to further aspects, the ML accelerator and microcontroller arefurther designed to process the video stream captured through thecamera.

According to further aspects, the one or more processors are designed tofurther send the processed data using ML accelerator via the lowdata-rate transceiver.

According to further aspects, the one or more processors are designed toreceive the data from the database via the low data-rate transceiver.

According to further aspects, the AI device is also used for trainingother AI models using an AI training platform.

According to further aspects, the ML accelerator communicates with thedevice server to register on the device network with programmed softwareand hardware.

According to further aspects, the data visualization identifies thevideo data and communicates with the AI device via the device server.

According to further aspects, the ML accelerator finds the recognizablepattern through ML computing.

According to further aspects, the video recognition information from thedatabase is communicated to the AI device via low data-rate gateway/basestations by the low data-rate transceiver.

According to further aspects, the low data-rate transceiver sends a lowdata-rate packet through gateway/base stations to the device server.

According to further aspects, the internal configuration of the AIdevice is changed as per instructions by the device server.

According to further aspects, the communication between the AI deviceand Data visualization is a secured session.

According to further aspects, the authentication is established using akey between the AI device and data visualization.

According to further aspects, the key can be a random number and/orcombination of characters and numbers.

According to further aspects, the data visualization subscribes to thedata sent by the AI device. The AI device signs the data using the keyand after signing data is published by the AI device towards the datavisualization by the AI device.

According to further aspects, the video recognition is established onboth the ends of the communication between the AI device and datavisualization.

According to further aspects, the data is MQTT encrypted when sentand/or received from the device server to and/or received from the datavisualization.

According to further aspects, the communication between the deviceserver and low data-rate transceiver takes place by sending and/orreceiving low data-rate protocol encrypted packets.

According to further aspects, the on-board communication between MLaccelerator and the low data-rate transceiver is on-board.

According to further aspects, the video stream recognition informationis generated by the data visualization.

According to some aspects, an apparatus for an AI device is disclosed.Further, the apparatus may include a camera to record video stream/data,a machine learning (ML) accelerator for video recognition, a lowdata-rate transceiver to send and/or receive data from the device serverconnected to one or more antenna, a microcontroller to control thefunctions such as sending/receiving data to/from low, a data-ratetransceiver, an internal memory (e.g., ROM), an interface to connectexternal software/hardware, a low data-rate gateways/base stations, adevice server, a database for storing all the data, and datavisualization.

According to further aspects, the camera can be any camera used tocapture video streaming.

According to further aspects, the ML accelerator is used for videorecognition using the captured data.

According to further aspects, the low data-rate transceiversends/receives data using low data-rate radiofrequency.

According to further aspects, the microcontroller processes the datawith an ML accelerator and sends the processed data using the lowdata-rate transceiver.

According to further aspects, the internal memory contains segments ofdata that can be accessed in real time.

According to further aspects, the processed data is sent to and/orreceived from the device server using low data-rate gateway/basestations.

According to further aspects, the device server connects with thedatabase through the internet.

According to further aspects, the database contains the processed datathat is utilized for video recognition. The data is also used to trainsimilar AI models.

According to further aspects, the data visualization is where the AIdevice sends a small amount of processed data to cloud for datavisualization.

According to further aspects, the data processed is sent through asecured session. The authentication session is established and data canonly be shared using a key.

According to some aspects, an apparatus for a device is disclosed.Further, the apparatus comprises an AI device for capturing andprocessing video stream using ML accelerator, a low data-ratetransceiver to send/receive processed data using low data-rate RF, a lowdata-rate gateway/base stations as means for sending/receiving data, adevice server mediator for connection between the device and the cloud,and a data visualization for visualizing processed data and videorecognition.

According to further aspects, a secure session is established betweenthe AI device and the data visualization by sharing a key. According tofurther aspects, a key can be any random number or combination ofcharacters and numbers.

According to further aspects, the data visualization subscribes to thedata sent by the AI device. The AI device signs the data using the key.

According to further aspects, after signing data is published by the AIdevice towards the data visualization by the AI device.

According to further aspects, the data is MQTT encrypted when sentand/or received from the device server to and/or received from the datavisualization.

According to further aspects, the processed data is sent to the datavisualization for video recognition.

According to further aspects, the information regarding videorecognition is communicated through data visualization.

FIG. 11 is an illustration of an online platform 1100 consistent withvarious embodiments of the present disclosure. By way of non-limitingexample, the online platform 1100 to facilitate managing incidentsoccurring in areas monitored by low data-rate monitoring devices usingthe low data-rate monitoring devices may be hosted on a centralizedserver 1102, such as, for example, a cloud computing service. Thecentralized server 1102 may communicate with other network entities,such as, for example, a mobile device 1106 (such as a smartphone, alaptop, a tablet computer, etc.), other electronic devices 1110 (such asdesktop computers, server computers, etc.), databases 1114, and sensors1116 over a communication network 1104, such as, but not limited to, theInternet. Further, users of the online platform 1100 may includerelevant parties such as, but not limited to, end-users, administrators,service providers, service consumers, and so on. Accordingly, in someinstances, electronic devices operated by the one or more relevantparties may be in communication with the platform.

A user 1112, such as the one or more relevant parties, may access onlineplatform 1100 through a web based software application or browser. Theweb based software application may be embodied as, for example, but notbe limited to, a website, a web application, a desktop application, anda mobile application compatible with a computing device 1800.

FIG. 12 is a block diagram of a system 1200 for facilitating managingincidents occurring in areas monitored by low data-rate monitoringdevices using the low data-rate monitoring devices, in accordance withsome embodiments. Further, the system 1200 may include a processor 1202,a device server 1204, and a data visualization device 1206.

Further, the processor 1202 may be configured to be communicativelycoupled with at least one low data-rate monitoring device 1302, as shownin FIG. 13. Further, the at least one low data-rate monitoring device1302 may include security cameras, fire detection cameras, parkingcameras, etc. Further, the least one low data-rate monitoring device1302 may include at least one camera 1304, as shown in FIG. 13, and alow data-rate transceiver 1306, as shown in FIG. 13. Further, the atleast one camera 1304 and the low data-rate transceiver 1306 may becommunicatively coupled. Further, the at least one camera 1304 may beconfigured for capturing at least one video of at least one area.Further, the at least one video may include at least one image. Further,the processor 1202 may include a machine learning (ML) hardwareaccelerator. Further, the ML hardware accelerator may be configured forperforming machine learning processing of the at least one video forperforming video recognition based on the capturing. Further, theprocessor 1202 may be configured for generating processed data based onthe performing of the machine learning processing. Further, theprocessed data may include a result obtained from the at least one videobased on the performing of the machine learning processing.

Further, the device server 1204 may be communicatively coupled with theleast one low data-rate monitoring device 1302. Further, the lowdata-rate transceiver 1306 may be configured for transmitting theprocessed data to the device server 1204. Further, the device server1204 may be configured for receiving the processed data based on thetransmitting of the processed data. Further, the device server 1204 maybe configured for transmitting a notification to at least one device.Further, in an embodiment, the at least one device may include apresentation device. Further, the presentation device may be configuredfor presenting the notification to at least one user.

Further, the data visualization device 1206 may be communicativelycoupled with the device server 1204. Further, the data visualizationdevice 1206 may be configured for visualizing the processed data.Further, the data visualization device 1206 may be configured foridentifying an incident in the at least one area based on thevisualizing. Further, the incident may include a theft incident, a fireincident, a wrong parking incident, a trespassing incident, etc.Further, the data visualization device 1206 may be configured forgenerating the notification for the incident based on the identifying.

In further embodiments, a controller 1502, as shown in FIG. 15, may becommunicatively coupled with the processor 1202. Further, controller mayinclude a microcontroller. Further, the controller 1502 may beconfigured for activating the ML hardware accelerator based on a MLaccelerator schedule. Further, the performing of the machine learningprocessing of the at least one video may be based on the activating ofthe ML hardware accelerator. Further, a memory device 1504, as shown inFIG. 15, may be communicatively coupled with the controller 1502.Further, the memory device 1504 may be configured for storing the MLaccelerator schedule. Further, in an embodiment, the controller 1502 maybe communicatively coupled with the low data-rate transceiver 1306.Further, the controller 1502 may be configured for activating the lowdata-rate transceiver 1306 based on a transceiver schedule. Further, thetransmitting of the processed data may be based on the activating of thelow data-rate transceiver 1306. Further, the memory device 1504 may beconfigured for storing the transceiver schedule.

Further, in some embodiments, the at least one camera 1304 may include aplurality of first cameras. Further, the plurality of first cameras maybe configured for collectively capturing a plurality of first videos.Further, the at least one video may include the plurality of firstvideos.

Further, in some embodiments, the at least one camera 1304 may include aplurality of second cameras. Further, the plurality of second camerasmay be configured for independently capturing a plurality of secondvideos. Further, the at least one video may include the plurality ofsecond videos.

Further, in some embodiments, the low data-rate transceiver 1306 may beassociated with a low data-rate radiofrequency. Further, the lowdata-rate transceiver 1306 uses the low data-rate radiofrequency for thetransmitting of the processed data to the device server 1204

In further embodiments, a database 1402, as shown in FIG. 14, may becommunicatively coupled with the device server 1204. Further, thedatabase 1402 may be configured for storing the processed data.

In further embodiments, a controller (not shown) may be communicativelycoupled with the at least one low data-rate monitoring device 1302.Further, the device server 1204 may be configured for receiving at leastcommand from the at least one device. Further, the controller may beconfigured for modifying at least one internal configuration of the atleast one low data-rate monitoring device 1302. Further, the at leastone internal configuration may include an active time, an activeduration, a sleep time, a sleep duration, etc. of the at least one lowdata-rate monitoring device 1302. Further, at least one of the capturingof the at least one video and the transmitting of the processed data maybe based on the modifying.

Further, in some embodiments, the at least one low data-rate monitoringdevice 1302 may include an internal memory. Further, the internal memorymay be communicatively coupled with the at least one camera 1304 and thelow data-rate transceiver 1306. Further, the internal memory may beconfigured for storing at least one of the at least one video and theprocessed data.

In further embodiments, a low data-rate gateway 1602, as shown in FIG.16, may be communicatively coupled with the at least one low data-ratemonitoring device 1302 and the device server 1204. Further, the lowdata-rate transceiver 1306 may be configured for communicating with thedevice server 1204 over an internet using the low data-rate gateway1602. Further, the transmitting of the processed data may be based onthe communicating.

Further, in some embodiments, the ML hardware accelerator may beconfigured for segmenting the at least one video. Further, the MLhardware accelerator may be configured for generating one or moresegments of the at least one video based on the segmenting. Further, theML hardware accelerator may be configured for determining a relevance ofthe one or more segments based on the performing of the machine learningprocessing of the at least one video. Further, the ML hardwareaccelerator may be configured for identifying at least one segment ofthe one or more segments based on determining. Further, the generatingof the processed data may be based on the identifying. Further, aprocessed data size of the processed data may be smaller than a videodata size of the at least one video.

In further embodiments, an artificial intelligence (AI) trainingplatform may be communicatively coupled with the at least one lowdata-rate monitoring device 1302. Further, the AI training platform maybe configured for training one or more artificial intelligence (AI)models associated with the AI training platform using the processeddata. Further, the one or more AI models may be configured for incidentdetection based on the training. Further, the AI training platform maybe communicatively coupled with the database 1402. Further, the database1402 may be configured for storing the one or more AI models. Further,the visualizing of the processed data may include analyzing theprocessed data using a AI model of the one or more AI models. Further,the identifying of the incident may be based on the analyzing of theprocessed data using the AI model.

FIG. 13 is a block diagram of the system 1200 with the at least one lowdata-rate monitoring device 1302, in accordance with some embodiments.

FIG. 14 is a block diagram of the system 1200, in accordance with someembodiments.

FIG. 15 is a block diagram of the system 1200, in accordance with someembodiments.

FIG. 16 is a block diagram of the system 1200 with the at least one lowdata-rate monitoring device 1302, in accordance with some embodiments.

FIG. 17 is a block diagram of a system 1700 for facilitating managingincidents occurring in areas monitored by low data-rate monitoringdevices using the low data-rate monitoring devices, in accordance withsome embodiments. Further, the system 1700 may include a processor 1702,a device server 1704, a data visualization device 1706, a controller1708, and a memory device 1710.

Further, the processor 1702 may be configured to be communicativelycoupled with at least one low data-rate monitoring device. Further, theleast one low data-rate monitoring device may include at least onecamera and a low data-rate transceiver. Further, the at least one cameraand the low data-rate transceiver may be communicatively coupled.Further, the at least one camera may be configured for capturing atleast one video of at least one area. Further, the processor 1702 mayinclude a machine learning (ML) hardware accelerator. Further, the MLhardware accelerator may be configured for performing machine learningprocessing of the at least one video for performing video recognitionbased on the capturing. Further, the processor 1702 may be configuredfor generating processed data based on the performing of the machinelearning processing.

Further, the device server 1704 may be communicatively coupled with theleast one low data-rate monitoring device. Further, the low data-ratetransceiver may be configured for transmitting the processed data to thedevice server 1704. Further, the device server 1704 may be configuredfor receiving the processed data based on the transmitting of theprocessed data. Further, the device server 1704 may be configured fortransmitting a notification to at least one device.

Further, the data visualization device 1706 may be communicativelycoupled with the device server 1704. Further, the data visualizationdevice 1706 may be configured for visualizing the processed data.Further, the data visualization device 1706 may be configured foridentifying an incident in the at least one area based on thevisualizing. Further, the data visualization device 1706 may beconfigured for generating the notification for the incident based on theidentifying.

Further, the controller 1708 may be communicatively coupled with theprocessor 1702. Further, the controller 1708 may be configured foractivating the ML hardware accelerator based on a ML acceleratorschedule. Further, the performing of the machine learning processing ofthe at least one video may be based on the activating of the ML hardwareaccelerator.

Further, the memory device 1710 may be communicatively coupled with thecontroller 1708. Further, the memory device 1710 may be configured forstoring the ML accelerator schedule.

Further, in some embodiments, the controller 1708 may be communicativelycoupled with the low data-rate transceiver. Further, the controller 1708may be configured for activating the low data-rate transceiver based ona transceiver schedule. Further, the transmitting of the processed datamay be based on the activating of the low data-rate transceiver.Further, the memory device 1710 may be configured for storing thetransceiver schedule.

Further, in some embodiments, the at least one camera may include aplurality of first cameras. Further, the plurality of first cameras maybe configured for collectively capturing a plurality of first videos.Further, the at least one video may include the plurality of firstvideos.

Further, in some embodiments, the at least one camera may include aplurality of second cameras. Further, the plurality of second camerasmay be configured for independently capturing a plurality of secondvideos. Further, the at least one video may include the plurality ofsecond videos.

Further, in some embodiments, the low data-rate transceiver may beassociated with a low data-rate radiofrequency. Further, the lowdata-rate transceiver uses the low data-rate radiofrequency for thetransmitting of the processed data to the device server 1704.

In further embodiments, a database may be communicatively coupled withthe device server 1704. Further, the database may be configured forstoring the processed data Further, in some embodiments, the at leastone low data-rate monitoring device may include an internal memory.Further, the internal memory may be communicatively coupled with the atleast one camera and the low data-rate transceiver. Further, theinternal memory may be configured for storing at least one of the atleast one video and the processed data.

In further embodiments, a low data-rate gateway may be communicativelycoupled with the at least one low data-rate monitoring device and thedevice server 1704. Further, the low data-rate transceiver may beconfigured for communicating with the device server 1704 over aninternet using the low data-rate gateway. Further, the transmitting ofthe processed data may be based on the communicating.

With reference to FIG. 18, a system consistent with an embodiment of thedisclosure may include a computing device or cloud service, such ascomputing device 1800. In a basic configuration, computing device 1800may include at least one processing unit 1802 and a system memory 1804.Depending on the configuration and type of computing device, systemmemory 1804 may comprise, but is not limited to, volatile (e.g.random-access memory (RAM)), non-volatile (e.g. read-only memory (ROM)),flash memory, or any combination. System memory 1804 may includeoperating system 1805, one or more programming modules 1806, and mayinclude a program data 1807. Operating system 1805, for example, may besuitable for controlling computing device 1800's operation. In oneembodiment, programming modules 1806 may include image-processingmodule, machine learning module. Furthermore, embodiments of thedisclosure may be practiced in conjunction with a graphics library,other operating systems, or any other application program and is notlimited to any particular application or system. This basicconfiguration is illustrated in FIG. 18 by those components within adashed line 1808.

Computing device 1800 may have additional features or functionality. Forexample, computing device 1800 may also include additional data storagedevices (removable and/or non-removable) such as, for example, magneticdisks, optical disks, or tape. Such additional storage is illustrated inFIG. 18 by a removable storage 1809 and a non-removable storage 1810.Computer storage media may include volatile and non-volatile, removableand non-removable media implemented in any method or technology forstorage of information, such as computer-readable instructions, datastructures, program modules, or other data. System memory 1804,removable storage 1809, and non-removable storage 1810 are all computerstorage media examples (i.e., memory storage.) Computer storage mediamay include, but is not limited to, RAM, ROM, electrically erasableread-only memory (EEPROM), flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to storeinformation and which can be accessed by computing device 1800. Any suchcomputer storage media may be part of device 1800. Computing device 1800may also have input device(s) 1812 such as a keyboard, a mouse, a pen, asound input device, a touch input device, a location sensor, a camera, abiometric sensor, etc. Output device(s) 1814 such as a display,speakers, a printer, etc. may also be included. The aforementioneddevices are examples and others may be used.

Computing device 1800 may also contain a communication connection 1816that may allow device 1800 to communicate with other computing devices1818, such as over a network in a distributed computing environment, forexample, an intranet or the Internet. Communication connection 1816 isone example of communication media. Communication media may typically beembodied by computer readable instructions, data structures, programmodules, or other data in a modulated data signal, such as a carrierwave or other transport mechanism, and includes any information deliverymedia. The term “modulated data signal” may describe a signal that hasone or more characteristics set or changed in such a manner as to encodeinformation in the signal. By way of example, and not limitation,communication media may include wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, radiofrequency (RF), infrared, and other wireless media. The term computerreadable media as used herein may include both storage media andcommunication media.

As stated above, a number of program modules and data files may bestored in system memory 1804, including operating system 1805. Whileexecuting on processing unit 1802, programming modules 1806 (e.g.,application 1820 such as a media player) may perform processesincluding, for example, one or more stages of methods, algorithms,systems, applications, servers, databases as described above. Theaforementioned process is an example, and processing unit 1802 mayperform other processes. Other programming modules that may be used inaccordance with embodiments of the present disclosure may includemachine learning applications.

Generally, consistent with embodiments of the disclosure, programmodules may include routines, programs, components, data structures, andother types of structures that may perform particular tasks or that mayimplement particular abstract data types. Moreover, embodiments of thedisclosure may be practiced with other computer system configurations,including hand-held devices, general purpose graphics processor-basedsystems, multiprocessor systems, microprocessor-based or programmableconsumer electronics, application specific integrated circuit-basedelectronics, minicomputers, mainframe computers, and the like.Embodiments of the disclosure may also be practiced in distributedcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed computing environment, program modules may be located inboth local and remote memory storage devices.

Furthermore, embodiments of the disclosure may be practiced in anelectrical circuit comprising discrete electronic elements, packaged orintegrated electronic chips containing logic gates, a circuit utilizinga microprocessor, or on a single chip containing electronic elements ormicroprocessors. Embodiments of the disclosure may also be practicedusing other technologies capable of performing logical operations suchas, for example, AND, OR, and NOT, including but not limited tomechanical, optical, fluidic, and quantum technologies. In addition,embodiments of the disclosure may be practiced within a general-purposecomputer or in any other circuits or systems.

Embodiments of the disclosure, for example, may be implemented as acomputer process (method), a computing system, or as an article ofmanufacture, such as a computer program product or computer readablemedia. The computer program product may be a computer storage mediareadable by a computer system and encoding a computer program ofinstructions for executing a computer process. The computer programproduct may also be a propagated signal on a carrier readable by acomputing system and encoding a computer program of instructions forexecuting a computer process. Accordingly, the present disclosure may beembodied in hardware and/or in software (including firmware, residentsoftware, micro-code, etc.). In other words, embodiments of the presentdisclosure may take the form of a computer program product on acomputer-usable or computer-readable storage medium havingcomputer-usable or computer-readable program code embodied in the mediumfor use by or in connection with an instruction execution system. Acomputer-usable or computer-readable medium may be any medium that cancontain, store, communicate, propagate, or transport the program for useby or in connection with the instruction execution system, apparatus, ordevice.

The computer-usable or computer-readable medium may be, for example butnot limited to, an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system, apparatus, device, or propagationmedium. More specific computer-readable medium examples (anon-exhaustive list), the computer-readable medium may include thefollowing: an electrical connection having one or more wires, a portablecomputer diskette, a random-access memory (RAM), a read-only memory(ROM), an erasable programmable read-only memory (EPROM or Flashmemory), an optical fiber, and a portable compact disc read-only memory(CD-ROM). Note that the computer-usable or computer-readable mediumcould even be paper or another suitable medium upon which the program isprinted, as the program can be electronically captured, via, forinstance, optical scanning of the paper or other medium, then compiled,interpreted, or otherwise processed in a suitable manner, if necessary,and then stored in a computer memory.

Embodiments of the present disclosure, for example, are described abovewith reference to block diagrams and/or operational illustrations ofmethods, systems, and computer program products according to embodimentsof the disclosure. The functions/acts noted in the blocks may occur outof the order as shown in any flowchart. For example, two blocks shown insuccession may in fact be executed substantially concurrently or theblocks may sometimes be executed in the reverse order, depending uponthe functionality/acts involved.

While certain embodiments of the disclosure have been described, otherembodiments may exist. Furthermore, although embodiments of the presentdisclosure have been described as being associated with data stored inmemory and other storage mediums, data can also be stored on or readfrom other types of computer-readable media, such as secondary storagedevices, like hard disks, solid state storage (e.g., USB drive), or aCD-ROM, a carrier wave from the Internet, or other forms of RAM or ROM.Further, the disclosed methods' stages may be modified in any manner,including by reordering stages and/or inserting or deleting stages,without departing from the disclosure.

Although the present disclosure has been explained in relation to itspreferred embodiment, it is to be understood that many other possiblemodifications and variations can be made without departing from thespirit and scope of the disclosure.

What is claimed is:
 1. A system for facilitating managing incidentsoccurring in areas monitored by low data-rate monitoring devices usingthe low data-rate monitoring devices, the system comprising: a processorconfigured to be communicatively coupled with at least one low data-ratemonitoring device, wherein the least one low data-rate monitoring devicecomprises at least one camera and a low data-rate transceiver, whereinthe at least one camera and the low data-rate transceiver arecommunicatively coupled, wherein the at least one camera is configuredfor capturing at least one video of at least one area, wherein theprocessor comprises a machine learning (ML) hardware accelerator,wherein the ML hardware accelerator is configured for performing machinelearning processing of the at least one video for performing videorecognition based on the capturing, wherein the processor is configuredfor generating processed data based on the performing of the machinelearning processing; a device server communicatively coupled with theleast one low data-rate monitoring device, wherein the low data-ratetransceiver is configured for transmitting the processed data to thedevice server, wherein the device server is configured for: receivingthe processed data based on the transmitting of the processed data; andtransmitting a notification to at least one device; and a datavisualization device communicatively coupled with the device server,wherein the data visualization device is configured for: visualizing theprocessed data; identifying an incident in the at least one area basedon the visualizing; and generating the notification for the incidentbased on the identifying.
 2. The system of claim 1 further comprising acontroller communicatively coupled with the processor, wherein thecontroller is configured for activating the ML hardware acceleratorbased on a ML accelerator schedule, wherein the performing of themachine learning processing of the at least one video is based on theactivating of the ML hardware accelerator; and a memory devicecommunicatively coupled with the controller, wherein the memory deviceis configured for storing the ML accelerator schedule.
 3. The system ofclaim 2, wherein the controller is communicatively coupled with the lowdata-rate transceiver, wherein the controller is further configured foractivating the low data-rate transceiver based on a transceiverschedule, wherein the transmitting of the processed data is based on theactivating of the low data-rate transceiver, wherein the memory deviceis further configured for storing the transceiver schedule.
 4. Thesystem of claim 1, wherein the at least one camera comprises a pluralityof first cameras, wherein the plurality of first cameras are configuredfor collectively capturing a plurality of first videos, wherein the atleast one video comprises the plurality of first videos.
 5. The systemof claim 1, wherein the at least one camera comprises a plurality ofsecond cameras, wherein the plurality of second cameras are configuredfor independently capturing a plurality of second videos, wherein the atleast one video comprises the plurality of second videos.
 6. The systemof claim 1, wherein the low data-rate transceiver is associated with alow data-rate radiofrequency, wherein the low data-rate transceiver usesthe low data-rate radiofrequency for the transmitting of the processeddata to the device server.
 7. The system of claim 1 further comprising adatabase communicatively coupled with the device server, wherein thedatabase is configured for storing the processed data.
 8. The system ofclaim 1 further comprising a controller communicatively coupled with theat least one low data-rate monitoring device, wherein the device serveris further configured for receiving at least command from the at leastone device, wherein the controller is configured for modifying at leastone internal configuration of the at least one low data-rate monitoringdevice, wherein at least one of the capturing of the at least one videoand the transmitting of the processed data is based on the modifying. 9.The system of claim 1, wherein the at least one low data-rate monitoringdevice comprises an internal memory, wherein the internal memory iscommunicatively coupled with the at least one camera and the lowdata-rate transceiver, wherein the internal memory is configured forstoring at least one of the at least one video and the processed data.10. The system of claim 1 further comprising a low data-rate gatewaycommunicatively coupled with the at least one low data-rate monitoringdevice and the device server, wherein the low data-rate transceiver isconfigured for communicating with the device server over an internetusing the low data-rate gateway, wherein the transmitting of theprocessed data is based on the communicating.
 11. The system of claim 1,wherein the ML hardware accelerator is further configured for:segmenting the at least one video; generating one or more segments ofthe at least one video based on the segmenting; determining a relevanceof the one or more segments based on the performing of the machinelearning processing of the at least one video; and identifying at leastone segment of the one or more segments based on determining, whereinthe generating of the processed data is further based on theidentifying, wherein a processed data size of the processed data issmaller than a video data size of the at least one video.
 12. The systemof claim 1 further comprising an artificial intelligence (AI) trainingplatform communicatively coupled with the at least one low data-ratemonitoring device, wherein the AI training platform is configured fortraining one or more artificial intelligence (AI) models associated withthe AI training platform using the processed data, wherein the one ormore AI models is configured for incident detection based on thetraining, wherein the AI training platform is communicatively coupledwith the database, wherein the database is configured for storing theone or more AI models, wherein the visualizing of the processed datacomprises analyzing the processed data using a AI model of the one ormore AI models, wherein the identifying of the incident is further basedon the analyzing of the processed data using the AI model.
 13. A systemfor facilitating managing incidents occurring in areas monitored by lowdata-rate monitoring devices using the low data-rate monitoring devices,the system comprising: a processor configured to be communicativelycoupled with at least one low data-rate monitoring device, wherein theleast one low data-rate monitoring device comprises at least one cameraand a low data-rate transceiver, wherein the at least one camera and thelow data-rate transceiver are communicatively coupled, wherein the atleast one camera is configured for capturing at least one video of atleast one area, wherein the processor comprises a machine learning (ML)hardware accelerator, wherein the ML hardware accelerator is configuredfor performing machine learning processing of the at least one video forperforming video recognition based on the capturing, wherein theprocessor is configured for generating processed data based on theperforming of the machine learning processing; a device servercommunicatively coupled with the least one low data-rate monitoringdevice, wherein the low data-rate transceiver is configured fortransmitting the processed data to the device server, wherein the deviceserver is configured for: receiving the processed data based on thetransmitting of the processed data; and transmitting a notification toat least one device; a data visualization device communicatively coupledwith the device server, wherein the data visualization device isconfigured for: visualizing the processed data; identifying an incidentin the at least one area based on the visualizing; and generating thenotification for the incident based on the identifying; a controllercommunicatively coupled with the processor, wherein the controller isconfigured for activating the ML hardware accelerator based on a MLaccelerator schedule, wherein the performing of the machine learningprocessing of the at least one video is based on the activating of theML hardware accelerator; and a memory device communicatively coupledwith the controller, wherein the memory device is configured for storingthe ML accelerator schedule.
 14. The system of claim 13, wherein thecontroller is communicatively coupled with the low data-ratetransceiver, wherein the controller is further configured for activatingthe low data-rate transceiver based on a transceiver schedule, whereinthe transmitting of the processed data is based on the activating of thelow data-rate transceiver, wherein the memory device is furtherconfigured for storing the transceiver schedule.
 15. The system of claim13, wherein the at least one camera comprises a plurality of firstcameras, wherein the plurality of first cameras are configured forcollectively capturing a plurality of first videos, wherein the at leastone video comprises the plurality of first videos.
 16. The system ofclaim 13, wherein the at least one camera comprises a plurality ofsecond cameras, wherein the plurality of second cameras are configuredfor independently capturing a plurality of second videos, wherein the atleast one video comprises the plurality of second videos.
 17. The systemof claim 13, wherein the low data-rate transceiver is associated with alow data-rate radiofrequency, wherein the low data-rate transceiver usesthe low data-rate radiofrequency for the transmitting of the processeddata to the device server.
 18. The system of claim 13 further comprisinga database communicatively coupled with the device server, wherein thedatabase is configured for storing the processed data.
 19. The system ofclaim 13, wherein the at least one low data-rate monitoring devicecomprises an internal memory, wherein the internal memory iscommunicatively coupled with the at least one camera and the lowdata-rate transceiver, wherein the internal memory is configured forstoring at least one of the at least one video and the processed data.20. The system of claim 13 further comprising a low data-rate gatewaycommunicatively coupled with the at least one low data-rate monitoringdevice and the device server, wherein the low data-rate transceiver isconfigured for communicating with the device server over an internetusing the low data-rate gateway, wherein the transmitting of theprocessed data is based on the communicating.